Community Sentiment
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Do you agree with the AI automation risk score calculated for this occupation?
AI Automation Index
Will AI Replace Cooks, Short Order?
Task-level automation exposure for Cooks, Short Order: 11 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
22%
weighted task index
11 of 11 tasks scored
Median Salary
$35,880 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
High
138,650 people hold this job — size, not growth
Projected 2025–35
-5.2%
employment change
17,400 openings a year · BLS line item
Pay by state
BLS OEWS publishes 51 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$19,910 – $46,630
Highest medians
Arizona $46,630
Rhode Island $46,250
Massachusetts $44,290
Hawaii $43,110
California $42,930
Lowest medians
Pennsylvania $19,910
Indiana $21,940
Alabama $22,330
Puerto Rico $22,500
Guam $23,080
State figures come from the 35-2015 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 22%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 84%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 11%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 14%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 0/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
11 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 5%
1 tasks
Medium exposure (≥ 35%) 35%
4 tasks
Low exposure (below 35%) 60%
6 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#2187 · Core · 10% automatable
Clean food preparation equipment, work areas, and counters or tables.
physical manipulation — Food area cleaning requires physical labor
Clean food preparation areas, facilities, or equipment.
#18725 · Core · 40% automatable
Restock kitchen supplies, rotate food, and stamp the time and date on food in coolers.
data analysis — Inventory tracking and reorder logic largely automatable
Store supplies or goods in kitchens or storage areas. Maintain food, beverage, or equipment inventories.
#2192 · Core · 6% automatable
Complete orders from steam tables, placing food on plates and serving customers at tables or counters.
physical manipulation — Plating food is fine manual dexterity
Serve food or beverages. Arrange food for serving.
#2188 · Core · 40% automatable
Plan work on orders so that items served together are finished at the same time.
decision support — Timing coordination aided by AI; kitchen execution human
Coordinate timing of food production activities.
#2189 · Core · 6% automatable
Grill, cook, and fry foods such as french fries, eggs, and pancakes.
physical manipulation — Cooking requires hands-on manipulation of real food
Cook foods.
#18726 · Core · 6% automatable
Perform food preparation tasks, such as making sandwiches, carving meats, making soups or salads, baking breads or desserts, and brewing coffee or tea.
physical manipulation — Hands-on beverage prep in unpredictable kitchen space
Prepare foods for cooking or serving. Prepare hot or cold beverages. Prepare breads or doughs.
#18727 · Core · 10% automatable
Perform general cleaning activities in kitchen and dining areas.
physical manipulation — Food area cleaning requires physical labor
Clean food preparation areas, facilities, or equipment.
#2190 · Core · 38% automatable
Take orders from customers and cook foods requiring short preparation times, according to customer requirements.
interpersonal — Order taking automatable; customer rapport partly human
Cook foods. Take customer orders.
#2191 · Supplemental · 6% automatable
Grill and garnish hamburgers or other meats, such as steaks and chops.
physical manipulation — Cooking requires hands-on manipulation of real food
Cook foods.
#2194 · Supplemental · 42% automatable
Order supplies and stock them on shelves.
decision support — Ordering via systems automatable; human approves spend
Store supplies or goods in kitchens or storage areas. Order materials, supplies, or equipment.
#2195 · Supplemental · 70% automatable
Accept payments, and make change or write charge slips as necessary.
data analysis — Billing and payment processing largely automated
Process customer bills or payments.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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